Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio

• Published in: PloS one Vol. 18; no. 4; p. e0284619
• Main Authors: Hamraz, Muhammad, Ali, Amjad, Mashwani, Wali Khan, Aldahmani, Saeed, Khan, Zardad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.04.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Biology and Life Sciences; Classification; Computer and Information Sciences; Computer applications; Data analysis; Data mining; Datasets; DNA microarrays; Engineering and Technology; Evaluation; Feature selection; Gene Expression; Gene Expression Profiling - methods; Genes; Genetic research; Genomes; Medicine and Health Sciences; Methods; Microarray Analysis; Research and Analysis Methods; Signal to noise ratio; Simulation analysis; Standard deviation; Support vector machines; Pakistan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0284619

Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio ( W SNR ) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method ( W SNR ) are also compared with four well known feature selection methods. We found that the ( W SNR ) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that ( W SNR ) outperforms all the other methods included in the study.